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The iterative weighted shrinkage-thresholding algorithm (IWSTA) has shownsuperiority to the classic unweighted iterative shrinkage-thresholdingalgorithm (ISTA) for solving linear inverse problems, which address theattributes differently. This paper proposes a new entropy regularized IWSTA(ERIWSTA) that adds an entropy regularizer to the cost function to measure theuncertainty of the weights to stimulate attributes to participate in problemsolving. Then, the weights are solved with a Lagrange multiplier method toobtain a simple iterative update. The weights can be explained as theprobability of the contribution of an attribute to the problem solution.Experimental results on CT image restoration show that the proposed method hasbetter performance in terms of convergence speed and restoration accuracy thanthe existing methods.

Entropy Regularized Iterative Weighted Shrinkage-Thresholding Algorithm (ERIWSTA): An Application to CT Image Restoration